We present the accuracy and performance results on the new EfficientNet-X model family on TPUs and GPUs, to demonstrate the effectiveness of the unified NAS and LACS method. Table REF  shows the speed and accuracy on ImageNet [1]} of EfficientNet-X models and comparisons with other SOTA CNN models, where a few key observations can be made. First, EfficientNet-X models are the fastest among each model group on TPUs and GPUs, with comparable accuracy. Specifically, EfficientNet-X models are up to more than 2X faster than EfficientNet, with geometric mean speedup of 56% and 83% on TPUv3 and GPUv100 respectively. EfficientNet-X is on average (geomean) 82% and 48% faster than RegNet and ResNeSt respectively on GPUv100 and 7X and 48% faster than RegNet and ResNeSt respectively on TPUv3. Second, all models except for Efficient-X models in Table REF  are polarized.
On one extreme, the EfficientNet family has the fewest FLOPs but the lowest operational intensity \(I\) . On the other extreme, other models such as ResNet and Inception families have the highest operational intensity but most FLOPs. Note that while lower FLOPs improves inference speed, lower operational intensity hurts inference speed. In contrast, the EfficientNet-X models strike a balance between computation load and computation rate, having both FLOPs and operational intensity in the middle between the two extremes, which makes EfficientNet-X models to be the fastest in each group.
